Abstract

The integration of Artificial Intelligence (AI) in education has paved the way for more personalized and scalable professional development opportunities. One significant innovation in this space is the use of automated feedback systems to support teacher self-assessment. These systems leverage machine learning algorithms, natural language processing, and data analytics to provide timely, evidence-based feedback to educators, helping them reflect on their instructional practices, classroom interactions, and professional growth areas. This chapter explores the theoretical foundations, design principles, and practical implementations of automated feedback systems in educational settings. It also examines the benefits and limitations of these technologies, considering ethical, technical, and pedagogical dimensions. Through a review of existing platforms, case studies, and emerging trends, this chapter highlights how AI-driven feedback tools can enhance self-regulation, foster continuous improvement, and contribute to a culture of reflective practice among educators.